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In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France's global Land Data Assimilation System (LDAS-Monde) tool in order to jointly analyse soil moisture and leaf area index (LAI). For the first time, observation operators based on neural networks (NNs) are trained with ISBA simulations and LAI observations from the PROBA-V satellite to predict the ASCAT backscatter signal. The trained NN-based observation operators are implemented in LDAS-Monde, which allows the sequential assimilation of backscatter observations. The impact of the assimilation is evaluated over southwestern France. The simulated and analysed backscatter signal, surface soil moisture, and LAI are evaluated using satellite observations from ASCAT and PROBA-V as well as in situ soil moisture observations. An overall improvement in the variables is observed when comparing the analysis with the open-loop simulation. The impact of the assimilation is greater over agricultural areas. [ABSTRACT FROM AUTHOR]

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of leaf area index (LAI) and surface soil moisture (SSM) on satellite-derived radar backscatter (sigma0) observations over southwestern France. Observations from the Advanced Scatterometer (ASCAT) are compared to simulated sigma0 values produced by the Water Cloud Model (WCM) coupled to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM). The LAI and SSM variables used by the WCM are derived from satellite observations and from ISBA simulations, respectively. Values of four parameters of the WCM are calibrated. The ability of the WCM to simulate ASCAT sigma0 observations in contrasting land cover conditions is explored. The response of observed and simulated sigma0 to LAI and SSM is analyzed across seasons. Two ways of representing vegetation in the WCM are compared. The impact of the Klaus storm on the ASCAT observations over the Les Landes forest in January 2009 is examined. The difference in sigma0 between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm and hardly any seasonality during four years after the storm. From 2013 onward, a regeneration phase is visible, with the return of seasonality in the forest vs. non-forest sigma0 difference. The ability of the WCM to represent this phenomenon is investigated. It is shown that the WCM can be used as an observation operator for the assimilation of ASCAT sigma0 observations into the ISBA LSM. [ABSTRACT FROM AUTHOR]